├── .vscode └── settings.json ├── test ├── env.d.ts ├── tsconfig.json └── convertMessagesToFalPrompt.test.ts ├── .prettierrc ├── .cursorrules ├── .editorconfig ├── vitest.config.mts ├── package.json ├── wrangler.jsonc ├── tsconfig.json ├── .gitignore ├── README.md ├── .cursor └── rules │ └── fal-openai-proxy-testing.mdc ├── src └── index.ts └── pnpm-lock.yaml /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "files.associations": { 3 | "wrangler.json": "jsonc" 4 | } 5 | } -------------------------------------------------------------------------------- /test/env.d.ts: -------------------------------------------------------------------------------- 1 | declare module 'cloudflare:test' { 2 | interface ProvidedEnv extends Env {} 3 | } 4 | -------------------------------------------------------------------------------- /.prettierrc: -------------------------------------------------------------------------------- 1 | { 2 | "printWidth": 140, 3 | "singleQuote": true, 4 | "semi": true, 5 | "useTabs": true 6 | } 7 | -------------------------------------------------------------------------------- /.cursorrules: -------------------------------------------------------------------------------- 1 | - Framework: Cloudflare Worker + Typescript 2 | - All Cloudflare Configuration should use wrangler.jsonc format, refer https://developers.cloudflare.com/workers/wrangler/configuration/ 3 | -------------------------------------------------------------------------------- /.editorconfig: -------------------------------------------------------------------------------- 1 | # http://editorconfig.org 2 | root = true 3 | 4 | [*] 5 | indent_style = tab 6 | end_of_line = lf 7 | charset = utf-8 8 | trim_trailing_whitespace = true 9 | insert_final_newline = true 10 | 11 | [*.yml] 12 | indent_style = space 13 | -------------------------------------------------------------------------------- /test/tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "extends": "../tsconfig.json", 3 | "compilerOptions": { 4 | "types": ["@cloudflare/workers-types/experimental", "@cloudflare/vitest-pool-workers"] 5 | }, 6 | "include": ["./**/*.ts", "../worker-configuration.d.ts"], 7 | "exclude": [] 8 | } 9 | -------------------------------------------------------------------------------- /vitest.config.mts: -------------------------------------------------------------------------------- 1 | import { defineWorkersConfig } from '@cloudflare/vitest-pool-workers/config'; 2 | 3 | export default defineWorkersConfig({ 4 | test: { 5 | poolOptions: { 6 | workers: { 7 | wrangler: { configPath: './wrangler.jsonc' }, 8 | }, 9 | }, 10 | }, 11 | }); 12 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "fal-openai-proxy-cf", 3 | "version": "0.1.0", 4 | "private": true, 5 | "scripts": { 6 | "deploy": "wrangler deploy", 7 | "dev": "wrangler dev", 8 | "start": "wrangler dev", 9 | "test": "vitest", 10 | "cf-typegen": "wrangler types" 11 | }, 12 | "devDependencies": { 13 | "@cloudflare/vitest-pool-workers": "^0.8.36", 14 | "@cloudflare/workers-types": "^4.20250605.0", 15 | "typescript": "^5.8.3", 16 | "vite": "6.2.7", 17 | "vitest": "~3.1.3", 18 | "wrangler": "^4.19.1" 19 | }, 20 | "dependencies": { 21 | "@fal-ai/client": "^1.5.0" 22 | } 23 | } 24 | -------------------------------------------------------------------------------- /wrangler.jsonc: -------------------------------------------------------------------------------- 1 | /** 2 | * For more details on how to configure Wrangler, refer to: 3 | * https://developers.cloudflare.com/workers/wrangler/configuration/ 4 | */ 5 | { 6 | "$schema": "node_modules/wrangler/config-schema.json", 7 | "name": "fal-openai", 8 | "main": "src/index.ts", 9 | "compatibility_date": "2025-04-12", 10 | "observability": { 11 | "enabled": true 12 | } 13 | /** 14 | * Smart Placement 15 | * Docs: https://developers.cloudflare.com/workers/configuration/smart-placement/#smart-placement 16 | */ 17 | // "placement": { "mode": "smart" }, 18 | 19 | /** 20 | * Bindings 21 | * Bindings allow your Worker to interact with resources on the Cloudflare Developer Platform, including 22 | * databases, object storage, AI inference, real-time communication and more. 23 | * https://developers.cloudflare.com/workers/runtime-apis/bindings/ 24 | */ 25 | 26 | /** 27 | * Environment Variables 28 | * https://developers.cloudflare.com/workers/wrangler/configuration/#environment-variables 29 | */ 30 | // "vars": { "MY_VARIABLE": "production_value" }, 31 | /** 32 | * Note: Use secrets to store sensitive data. 33 | * https://developers.cloudflare.com/workers/configuration/secrets/ 34 | */ 35 | 36 | /** 37 | * Static Assets 38 | * https://developers.cloudflare.com/workers/static-assets/binding/ 39 | */ 40 | // "assets": { "directory": "./public/", "binding": "ASSETS" }, 41 | 42 | /** 43 | * Service Bindings (communicate between multiple Workers) 44 | * https://developers.cloudflare.com/workers/wrangler/configuration/#service-bindings 45 | */ 46 | // "services": [{ "binding": "MY_SERVICE", "service": "my-service" }] 47 | } 48 | -------------------------------------------------------------------------------- /tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | /* Visit https://aka.ms/tsconfig.json to read more about this file */ 4 | 5 | /* Set the JavaScript language version for emitted JavaScript and include compatible library declarations. */ 6 | "target": "es2021", 7 | /* Specify a set of bundled library declaration files that describe the target runtime environment. */ 8 | "lib": ["es2021"], 9 | /* Specify what JSX code is generated. */ 10 | "jsx": "react-jsx", 11 | 12 | /* Specify what module code is generated. */ 13 | "module": "es2022", 14 | /* Specify how TypeScript looks up a file from a given module specifier. */ 15 | "moduleResolution": "Bundler", 16 | /* Specify type package names to be included without being referenced in a source file. */ 17 | "types": [ 18 | "@cloudflare/workers-types/2023-07-01" 19 | ], 20 | /* Enable importing .json files */ 21 | "resolveJsonModule": true, 22 | 23 | /* Allow JavaScript files to be a part of your program. Use the `checkJS` option to get errors from these files. */ 24 | "allowJs": true, 25 | /* Enable error reporting in type-checked JavaScript files. */ 26 | "checkJs": false, 27 | 28 | /* Disable emitting files from a compilation. */ 29 | "noEmit": true, 30 | 31 | /* Ensure that each file can be safely transpiled without relying on other imports. */ 32 | "isolatedModules": true, 33 | /* Allow 'import x from y' when a module doesn't have a default export. */ 34 | "allowSyntheticDefaultImports": true, 35 | /* Ensure that casing is correct in imports. */ 36 | "forceConsistentCasingInFileNames": true, 37 | 38 | /* Enable all strict type-checking options. */ 39 | "strict": true, 40 | 41 | /* Skip type checking all .d.ts files. */ 42 | "skipLibCheck": true 43 | }, 44 | "exclude": ["test"], 45 | "include": ["worker-configuration.d.ts", "src/**/*.ts"] 46 | } 47 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Logs 2 | 3 | logs 4 | _.log 5 | npm-debug.log_ 6 | yarn-debug.log* 7 | yarn-error.log* 8 | lerna-debug.log* 9 | .pnpm-debug.log* 10 | 11 | # Diagnostic reports (https://nodejs.org/api/report.html) 12 | 13 | report.[0-9]_.[0-9]_.[0-9]_.[0-9]_.json 14 | 15 | # Runtime data 16 | 17 | pids 18 | _.pid 19 | _.seed 20 | \*.pid.lock 21 | 22 | # Directory for instrumented libs generated by jscoverage/JSCover 23 | 24 | lib-cov 25 | 26 | # Coverage directory used by tools like istanbul 27 | 28 | coverage 29 | \*.lcov 30 | 31 | # nyc test coverage 32 | 33 | .nyc_output 34 | 35 | # Grunt intermediate storage (https://gruntjs.com/creating-plugins#storing-task-files) 36 | 37 | .grunt 38 | 39 | # Bower dependency directory (https://bower.io/) 40 | 41 | bower_components 42 | 43 | # node-waf configuration 44 | 45 | .lock-wscript 46 | 47 | # Compiled binary addons (https://nodejs.org/api/addons.html) 48 | 49 | build/Release 50 | 51 | # Dependency directories 52 | 53 | node_modules/ 54 | jspm_packages/ 55 | 56 | # Snowpack dependency directory (https://snowpack.dev/) 57 | 58 | web_modules/ 59 | 60 | # TypeScript cache 61 | 62 | \*.tsbuildinfo 63 | 64 | # Optional npm cache directory 65 | 66 | .npm 67 | 68 | # Optional eslint cache 69 | 70 | .eslintcache 71 | 72 | # Optional stylelint cache 73 | 74 | .stylelintcache 75 | 76 | # Microbundle cache 77 | 78 | .rpt2_cache/ 79 | .rts2_cache_cjs/ 80 | .rts2_cache_es/ 81 | .rts2_cache_umd/ 82 | 83 | # Optional REPL history 84 | 85 | .node_repl_history 86 | 87 | # Output of 'npm pack' 88 | 89 | \*.tgz 90 | 91 | # Yarn Integrity file 92 | 93 | .yarn-integrity 94 | 95 | # dotenv environment variable files 96 | 97 | .env 98 | .env.development.local 99 | .env.test.local 100 | .env.production.local 101 | .env.local 102 | 103 | # parcel-bundler cache (https://parceljs.org/) 104 | 105 | .cache 106 | .parcel-cache 107 | 108 | # Next.js build output 109 | 110 | .next 111 | out 112 | 113 | # Nuxt.js build / generate output 114 | 115 | .nuxt 116 | dist 117 | 118 | # Gatsby files 119 | 120 | .cache/ 121 | 122 | # Comment in the public line in if your project uses Gatsby and not Next.js 123 | 124 | # https://nextjs.org/blog/next-9-1#public-directory-support 125 | 126 | # public 127 | 128 | # vuepress build output 129 | 130 | .vuepress/dist 131 | 132 | # vuepress v2.x temp and cache directory 133 | 134 | .temp 135 | .cache 136 | 137 | # Docusaurus cache and generated files 138 | 139 | .docusaurus 140 | 141 | # Serverless directories 142 | 143 | .serverless/ 144 | 145 | # FuseBox cache 146 | 147 | .fusebox/ 148 | 149 | # DynamoDB Local files 150 | 151 | .dynamodb/ 152 | 153 | # TernJS port file 154 | 155 | .tern-port 156 | 157 | # Stores VSCode versions used for testing VSCode extensions 158 | 159 | .vscode-test 160 | 161 | # yarn v2 162 | 163 | .yarn/cache 164 | .yarn/unplugged 165 | .yarn/build-state.yml 166 | .yarn/install-state.gz 167 | .pnp.\* 168 | 169 | # wrangler project 170 | 171 | .dev.vars 172 | .wrangler/ 173 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # fal.ai OpenAI-compatible API Proxy for Cloudflare Workers 2 | 3 | 本项目提供了一个 Cloudflare Worker，将 fal.ai API 转成 OpenAI API 兼容格式。 4 | 5 | > [!WARNING] 6 | > 仅支持文本对话，不支持文件/图片上传 7 | 8 | ## 部署 9 | 10 | 1. **Fork 本仓库:** 点击 GitHub 页面右上角的 "Fork" 按钮，将此仓库复制到您的 GitHub 账户下。 11 | 2. **登录 Cloudflare:** 打开浏览器，访问 [Cloudflare Dashboard](https://dash.cloudflare.com/) 并登录您的账户。 12 | 3. **连接 GitHub 账户 (如果尚未连接):** 13 | * 在 Cloudflare Dashboard 中，导航至 "Workers & Pages"。 14 | * 点击 "概述 (Overview)" > "连接到 Git (Connect to Git)"。 15 | * 选择 GitHub 并按照提示授权 Cloudflare 访问您的 GitHub 仓库。 16 | 4. **创建 Worker 服务:** 17 | * 在 "Workers & Pages" 页面，点击 "创建应用程序 (Create application)" > "Pages" 选项卡 > "连接到 Git (Connect to Git)"。 18 | * 选择您 Fork 的仓库，点击 "开始设置 (Begin setup)"。 19 | * **项目名称 (Project name):** 输入您想要的 Worker 服务名称 (例如 `my-fal-proxy`)。 20 | * **设置构建和部署 (Set up builds and deployments):** 此部分所有设置保持默认即可。 21 | * 点击 "保存并部署 (Save and Deploy)"。 22 | 5. **配置环境变量:** 23 | * 部署完成后，导航至您新创建的 Worker 服务的设置页面。 24 | * 选择 "设置 (Settings)" > "变量 (Variables)"。 25 | * 在 "环境变量 (Environment Variables)" 部分，点击 "添加变量 (Add variable)"，添加以下两个变量： 26 | * `FAL_KEY`: 输入您从 fal.ai 获取的 API Key。勾选 "加密 (Encrypt)" 以保护密钥。 27 | * `API_KEY`: 输入您希望用于访问此代理服务的自定义 API Key (可以自行设置一个强密码)。勾选 "加密 (Encrypt)"。 28 | * 点击 "保存 (Save)"。 29 | 30 | 部署完成后，您的 Worker 服务 URL (例如 `your-worker-name.your-subdomain.workers.dev`) 即为 OpenAI 兼容的 API Endpoint。 31 | 32 | ## 本地开发 33 | 34 | 您可以使用 Cloudflare 的 Wrangler CLI 在本地进行开发和测试。您需要安装 [Node.js](https://nodejs.org/) (包含 npm)。 35 | 36 | 1. **登录 Wrangler:** 37 | ```bash 38 | npx wrangler login 39 | ``` 40 | 这将打开浏览器提示您登录 Cloudflare 账户。 41 | 2. **克隆仓库:** 42 | ```bash 43 | git clone 44 | cd 45 | ``` 46 | 3. **安装依赖:** 47 | ```bash 48 | npm install 49 | # 或者 50 | # yarn install 51 | ``` 52 | 4. **创建 `.dev.vars` 文件:** 53 | 在项目根目录下创建一个名为 `.dev.vars` 的文件，并添加您的环境变量： 54 | ``` 55 | FAL_KEY="your_fal_ai_key" 56 | API_KEY="your_custom_api_key" 57 | ``` 58 | 两个 KEY 都支持多个传入，通过英文逗号分隔，因此 API_KEY 中不能包含逗号 59 | 60 | **注意:** 不要将此文件提交到 Git 仓库。`.gitignore` 文件已配置忽略此文件。 61 | 5. **启动本地开发服务器:** 62 | ```bash 63 | npx wrangler dev 64 | ``` 65 | Wrangler 将启动一个本地服务器 (通常在 `http://localhost:8787`)，模拟 Cloudflare 环境。您可以使用此本地 URL 进行测试。 66 | 67 | ## 使用 68 | 69 | 将您的 OpenAI 客户端配置为使用您的 Cloudflare Worker URL 作为 API Base URL，并使用您在环境变量中设置的 `API_KEY` 作为 Bearer Token 进行身份验证。 70 | 71 | **示例 (使用 `curl`):** 72 | 73 | ```bash 74 | curl https://your-worker-name.your-subdomain.workers.dev/v1/chat/completions \ 75 | -H "Authorization: Bearer your_custom_api_key" \ 76 | -H "Content-Type: application/json" \ 77 | -d '{ 78 | "model": "fal-ai/Mixtral-8x7B-Instruct-v0.1", # 或其他 fal.ai 支持的模型 79 | "messages": [{"role": "user", "content": "你好!"}] 80 | }' 81 | ``` 82 | 83 | 将 `your-worker-name.your-subdomain.workers.dev` 替换为您的 Worker URL，并将 `your_custom_api_key` 替换为您设置的 `API_KEY`。将 `"fal-ai/Mixtral-8x7B-Instruct-v0.1"` 替换为您想使用的 fal.ai 模型。 84 | -------------------------------------------------------------------------------- /.cursor/rules/fal-openai-proxy-testing.mdc: -------------------------------------------------------------------------------- 1 | --- 2 | description: 3 | globs: 4 | alwaysApply: true 5 | --- 6 | # FAL OpenAI CF Proxy Testing Rules 7 | 8 | ## Testing Framework & Conventions 9 | 10 | ### Test File Naming 11 | - All test files must use `.test.ts` suffix 12 | - Test files should be located in the `test/` directory 13 | 14 | ### Testing Standards 15 | 16 | #### Character Limit Testing 17 | - **Production Character Limits**: 18 | - `SYSTEM_PROMPT_LIMIT = 4800` 19 | - **Note**: The actual character length limit is 5000, but 4800 is used with pre-reserved redundancy space 20 | - **No prompt character limit**: The `prompt` part does not have character restrictions 21 | 22 | #### Test Implementation Guidelines 23 | - **Use Source Functions**: Always import and test the actual functions from [src/index.ts](mdc:src/index.ts) rather than implementing duplicate test versions 24 | - **Character Limit Testing**: Use smaller test limits (100, 500, 1000) to simulate character overflow scenarios without using actual production limits 25 | - **Edge Case Coverage**: Include tests for: 26 | - Empty messages 27 | - Null content handling 28 | - System message overflow 29 | - Message truncation scenarios 30 | - Boundary conditions 31 | 32 | #### Core Function Testing: convertMessagesToFalPrompt 33 | 34 | The main function [convertMessagesToFalPrompt](mdc:src/index.ts) implements V3 logic with these characteristics: 35 | 36 | 1. **System Message Handling**: 37 | - Only the last system message is used 38 | - System message length is validated against SYSTEM_PROMPT_LIMIT 39 | - Returns error if system message exceeds limit 40 | 41 | 2. **Message Distribution**: 42 | - **prompt**: Contains last 3 message records (previous user + assistant + current user) 43 | - **system_prompt**: Contains system message + earlier chat history (if any) 44 | - Older messages are truncated if they exceed character limits 45 | 46 | 3. **Test Coverage Requirements**: 47 | - Basic message handling (system, user, assistant combinations) 48 | - Multi-turn conversation scenarios 49 | - Character limit enforcement with small test values 50 | - Null content and edge cases 51 | - Message truncation behavior 52 | 53 | #### Example Test Pattern 54 | ```typescript 55 | // Use small limits for testing character overflow 56 | it('应该处理字符限制测试', () => { 57 | const TEST_LIMIT = 100; // Small test limit 58 | const longMessage = 'a'.repeat(TEST_LIMIT + 10); 59 | // Test character overflow handling... 60 | }); 61 | ``` 62 | 63 | ### Function Import Pattern 64 | Always import from the main source file: 65 | ```typescript 66 | import { convertMessagesToFalPrompt, OpenAIMessage, SYSTEM_PROMPT_LIMIT } from '../src/index'; 67 | ``` 68 | 69 | ### Cloudflare Worker Context 70 | - Framework: Cloudflare Worker + TypeScript 71 | - Configuration: Use `wrangler.jsonc` format for all Cloudflare configurations 72 | - Test environment: Vitest with Cloudflare Workers runtime 73 | 74 | ## Character Limit Philosophy 75 | - Production limits are set at 4800 to provide safety buffer below the actual 5000 character limit 76 | - Tests should verify truncation and error handling without using production limit values 77 | - Focus on correctness of limit enforcement rather than specific numeric values 78 | -------------------------------------------------------------------------------- /test/convertMessagesToFalPrompt.test.ts: -------------------------------------------------------------------------------- 1 | /** 2 | * convertMessagesToFalPrompt 函数单元测试 3 | * 4 | * 核心逻辑：倒序遍历messages，至多取3条user/assistant消息放到prompt部分 5 | * 6 | * 实现逻辑： 7 | * 1. 分离系统消息和对话消息，只使用最后一个非空系统消息 8 | * 2. 倒序遍历，取最后3条消息，根据最后一条消息类型分两种情况： 9 | * a. 最后一条消息是user消息：取倒数第3、第2条作为chat_history（最多2条：user+assistant）， 10 | * 格式为 "\nAssistant: \n\n<最新用户消息>" 11 | * b. 最后一条消息不是user消息：取最后2条消息作为chat_history，放在system_prompt中， 12 | * 格式为 "\nAssistant: \n" 13 | * 3. prompt中会自动拼接Human消息，所以chat_history最多包含2条：user和assistant 14 | * 4. 最后一个用户消息是最新提问，不属于对话历史 15 | * 5. 系统消息如果超出SYSTEM_PROMPT_LIMIT则直接返回错误 16 | * 6. 只有非空内容的消息才会被处理 17 | */ 18 | 19 | import { describe, it, expect } from 'vitest'; 20 | import { convertMessagesToFalPrompt, OpenAIMessage } from '../src/index'; 21 | 22 | // Define the constant locally for testing purposes 23 | const SYSTEM_PROMPT_LIMIT = 4800; 24 | 25 | describe('convertMessagesToFalPrompt', () => { 26 | // === 基础测试用例 === 27 | 28 | it('应该正确处理只有系统消息的情况', () => { 29 | const messages: OpenAIMessage[] = [ 30 | { role: 'system', content: 'You are a helpful assistant.' } 31 | ]; 32 | 33 | const result = convertMessagesToFalPrompt(messages); 34 | 35 | expect(result.system_prompt).toBe('You are a helpful assistant.'); 36 | expect(result.prompt).toBe(''); 37 | expect(result.error).toBeUndefined(); 38 | }); 39 | 40 | it('应该正确处理空消息数组', () => { 41 | const messages: OpenAIMessage[] = []; 42 | 43 | const result = convertMessagesToFalPrompt(messages); 44 | 45 | expect(result.system_prompt).toBe(''); 46 | expect(result.prompt).toBe(''); 47 | expect(result.error).toBeUndefined(); 48 | }); 49 | 50 | it('应该正确处理只有用户消息的情况', () => { 51 | const messages: OpenAIMessage[] = [ 52 | { role: 'user', content: 'Hello, how are you?' } 53 | ]; 54 | 55 | const result = convertMessagesToFalPrompt(messages); 56 | 57 | expect(result.system_prompt).toBe(''); 58 | expect(result.prompt).toBe('Hello, how are you?'); 59 | expect(result.error).toBeUndefined(); 60 | }); 61 | 62 | it('应该正确处理最后一条消息不是用户消息的情况', () => { 63 | const messages: OpenAIMessage[] = [ 64 | { role: 'user', content: 'Hello' }, 65 | { role: 'assistant', content: 'Hi there!' } 66 | ]; 67 | 68 | const result = convertMessagesToFalPrompt(messages); 69 | 70 | // 倒序遍历至多取3条user/assistant消息放到prompt：Hi there! + Hello 71 | // 但最后一条消息不是用户消息，按照要求格式处理 72 | expect(result.system_prompt).toBe(''); 73 | expect(result.prompt).toBe('Hello\nAssistant: Hi there!'); 74 | expect(result.error).toBeUndefined(); 75 | }); 76 | 77 | it('应该正确处理有系统消息但最后一条不是用户消息的情况', () => { 78 | const messages: OpenAIMessage[] = [ 79 | { role: 'system', content: 'You are a helpful assistant.' }, 80 | { role: 'user', content: 'Hello' }, 81 | { role: 'assistant', content: 'Hi there!' } 82 | ]; 83 | 84 | const result = convertMessagesToFalPrompt(messages); 85 | 86 | // 倒序遍历至多取3条user/assistant消息放到prompt：Hi there! + Hello 87 | // 剩余的messages放到system_prompt：system 88 | // 但最后一条消息不是用户消息，按照要求格式处理 89 | expect(result.system_prompt).toBe('You are a helpful assistant.'); 90 | expect(result.prompt).toBe('Hello\nAssistant: Hi there!'); 91 | expect(result.error).toBeUndefined(); 92 | }); 93 | 94 | // === 核心逻辑测试用例 === 95 | 96 | it('应该正确处理系统消息+用户消息', () => { 97 | const messages: OpenAIMessage[] = [ 98 | { role: 'system', content: 'You are a helpful assistant.' }, 99 | { role: 'user', content: 'What is 2+2?' } 100 | ]; 101 | 102 | const result = convertMessagesToFalPrompt(messages); 103 | 104 | expect(result.system_prompt).toBe('You are a helpful assistant.'); 105 | expect(result.prompt).toBe('What is 2+2?'); 106 | expect(result.error).toBeUndefined(); 107 | }); 108 | 109 | it('应该正确处理三条消息（用户+助手+用户）- 核心逻辑', () => { 110 | const messages: OpenAIMessage[] = [ 111 | { role: 'system', content: 'You are a helpful assistant.' }, 112 | { role: 'user', content: 'What is 2+2?' }, 113 | { role: 'assistant', content: '2+2 equals 4.' }, 114 | { role: 'user', content: 'What about 3+3?' } 115 | ]; 116 | 117 | const result = convertMessagesToFalPrompt(messages); 118 | 119 | // 倒序遍历至多取3条user/assistant消息放到prompt：What about 3+3? + 2+2 equals 4. + What is 2+2? 120 | // 剩余的messages放到system_prompt：system 121 | expect(result.system_prompt).toBe('You are a helpful assistant.'); 122 | // prompt包含倒序的最后3条消息 123 | expect(result.prompt).toBe('What is 2+2?\nAssistant: 2+2 equals 4.\nWhat about 3+3?'); 124 | expect(result.error).toBeUndefined(); 125 | }); 126 | 127 | it('应该正确处理多条消息，历史对话放在prompt中', () => { 128 | const messages: OpenAIMessage[] = [ 129 | { role: 'system', content: 'You are a helpful assistant.' }, 130 | { role: 'user', content: 'First question' }, 131 | { role: 'assistant', content: 'First answer' }, 132 | { role: 'user', content: 'Second question' }, 133 | { role: 'assistant', content: 'Second answer' }, 134 | { role: 'user', content: 'Third question' } 135 | ]; 136 | 137 | const result = convertMessagesToFalPrompt(messages); 138 | 139 | // 倒序遍历至多取3条user/assistant消息放到prompt：Third question + Second answer + Second question 140 | // 剩余的messages放到system_prompt：system + First question + First answer 141 | expect(result.system_prompt).toBe('You are a helpful assistant.\nHuman: First question\nAssistant: First answer'); 142 | 143 | // prompt包含倒序的最后3条消息 144 | expect(result.prompt).toBe('Second question\nAssistant: Second answer\nThird question'); 145 | expect(result.error).toBeUndefined(); 146 | }); 147 | 148 | it('应该正确处理null content', () => { 149 | const messages: OpenAIMessage[] = [ 150 | { role: 'system', content: null }, 151 | { role: 'user', content: null }, 152 | { role: 'assistant', content: null }, 153 | { role: 'user', content: 'Hello' } 154 | ]; 155 | 156 | const result = convertMessagesToFalPrompt(messages); 157 | 158 | // 空消息被过滤后，只剩下最后一条有效的用户消息 159 | expect(result.system_prompt).toBe(''); 160 | expect(result.prompt).toBe('Hello'); 161 | expect(result.error).toBeUndefined(); 162 | }); 163 | 164 | // === 系统消息处理测试 === 165 | 166 | it('应该只使用最后一个系统消息', () => { 167 | const messages: OpenAIMessage[] = [ 168 | { role: 'system', content: 'First system message' }, 169 | { role: 'system', content: 'Second system message' }, 170 | { role: 'user', content: 'Hello' } 171 | ]; 172 | 173 | const result = convertMessagesToFalPrompt(messages); 174 | 175 | expect(result.system_prompt).toBe('Second system message'); 176 | expect(result.prompt).toBe('Hello'); 177 | expect(result.error).toBeUndefined(); 178 | }); 179 | 180 | it('应该在系统消息超出限制时返回错误', () => { 181 | const longSystemMessage = 'a'.repeat(SYSTEM_PROMPT_LIMIT + 100); 182 | const messages: OpenAIMessage[] = [ 183 | { role: 'system', content: longSystemMessage }, 184 | { role: 'user', content: 'Hello' } 185 | ]; 186 | 187 | const result = convertMessagesToFalPrompt(messages); 188 | 189 | expect(result.system_prompt).toBe(''); 190 | expect(result.prompt).toBe(''); 191 | expect(result.error).toContain('System message too long'); 192 | expect(result.error).toContain(`${longSystemMessage.length} characters exceeds limit of ${SYSTEM_PROMPT_LIMIT}`); 193 | }); 194 | 195 | // === 字符限制测试用例 === 196 | 197 | it('应该正确处理system_prompt在限值内的情况', () => { 198 | // 创建一个接近但不超过 4800 字符限制的系统消息 199 | const systemMessage = 'You are a helpful assistant. '.repeat(150); // 约 4200 字符 200 | const messages: OpenAIMessage[] = [ 201 | { role: 'system', content: systemMessage }, 202 | { role: 'user', content: 'Hello' }, 203 | { role: 'assistant', content: 'Hi there!' }, 204 | { role: 'user', content: 'How are you?' } 205 | ]; 206 | 207 | const result = convertMessagesToFalPrompt(messages); 208 | 209 | // 验证没有错误，且系统消息被正确处理 210 | expect(result.error).toBeUndefined(); 211 | expect(result.system_prompt).toBe(systemMessage.trim()); 212 | expect(result.system_prompt.length).toBeLessThanOrEqual(SYSTEM_PROMPT_LIMIT); 213 | 214 | // 倒序遍历至多取3条user/assistant消息放到prompt：How are you? + Hi there! + Hello 215 | expect(result.prompt).toBe('Hello\nAssistant: Hi there!\nHow are you?'); 216 | }); 217 | 218 | it('应该正确截断历史消息，选取最后一个assistant，截断 user 消息以适应字符限制', () => { 219 | // 创建会导致 system_prompt 超过限制的大量历史消息 220 | const systemMessage = 'You are a helpful assistant.'; 221 | const longMessage = 'This is a very long message that will help us test the truncation behavior. '.repeat(50); // 约 4000 字符 222 | 223 | const messages: OpenAIMessage[] = [ 224 | { role: 'system', content: systemMessage }, 225 | { role: 'user', content: longMessage }, 226 | { role: 'assistant', content: longMessage }, 227 | { role: 'user', content: longMessage }, 228 | { role: 'assistant', content: longMessage }, 229 | { role: 'user', content: 'Second question' }, 230 | { role: 'assistant', content: 'Second answer' }, 231 | { role: 'user', content: 'Final question' } 232 | ]; 233 | 234 | const result = convertMessagesToFalPrompt(messages); 235 | 236 | // 验证没有错误 237 | expect(result.error).toBeUndefined(); 238 | 239 | // 验证system_prompt不超过限制 240 | expect(result.system_prompt.length).toBeLessThanOrEqual(SYSTEM_PROMPT_LIMIT); 241 | 242 | // 倒序遍历至多取3条user/assistant消息放到prompt：Final question + Second answer + Second question 243 | // 剩余的messages放到system_prompt：system + longMessage(user) + longMessage(assistant) + longMessage(user) + longMessage(assistant) 244 | // 但由于字符限制，system_prompt会被截断，优先保留系统消息 245 | expect(result.system_prompt).toContain('You are a helpful assistant.'); 246 | // 由于longMessage太长，可能只能包含系统消息和部分assistant消息 247 | expect(result.system_prompt).toContain('Assistant:'); 248 | 249 | // prompt包含倒序的最后3条消息 250 | expect(result.prompt).toBe('Second question\nAssistant: Second answer\nFinal question'); 251 | }); 252 | 253 | it('应该在多个系统消息超出限制时仍然只检查最后一个', () => { 254 | const shortSystemMessage = 'Short system message'; 255 | const longSystemMessage = 'a'.repeat(SYSTEM_PROMPT_LIMIT + 100); 256 | 257 | const messages: OpenAIMessage[] = [ 258 | { role: 'system', content: shortSystemMessage }, 259 | { role: 'system', content: longSystemMessage }, // 这个会覆盖前面的 260 | { role: 'user', content: 'Hello' } 261 | ]; 262 | 263 | const result = convertMessagesToFalPrompt(messages); 264 | 265 | expect(result.error).toContain('System message too long'); 266 | expect(result.system_prompt).toBe(''); 267 | expect(result.prompt).toBe(''); 268 | }); 269 | 270 | it('应该正确处理复杂的多轮对话场景', () => { 271 | // 测试真实的多轮对话场景 272 | const systemMessage = 'You are a helpful AI assistant that provides detailed explanations.'; 273 | const user1 = 'Can you explain how machine learning works?'; 274 | const assistant1 = 'Machine learning is a subset of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed.'; 275 | const user2 = 'What are the main types of machine learning?'; 276 | const assistant2 = 'There are three main types: supervised learning, unsupervised learning, and reinforcement learning.'; 277 | const user3 = 'Can you give me an example of supervised learning?'; 278 | 279 | const messages: OpenAIMessage[] = [ 280 | { role: 'system', content: systemMessage }, 281 | { role: 'user', content: user1 }, 282 | { role: 'assistant', content: assistant1 }, 283 | { role: 'user', content: user2 }, 284 | { role: 'assistant', content: assistant2 }, 285 | { role: 'user', content: user3 } 286 | ]; 287 | 288 | const result = convertMessagesToFalPrompt(messages); 289 | 290 | // 验证基本正确性 291 | expect(result.error).toBeUndefined(); 292 | expect(result.system_prompt.length).toBeLessThanOrEqual(SYSTEM_PROMPT_LIMIT); 293 | 294 | // 倒序遍历至多取3条user/assistant消息放到prompt：user3 + assistant2 + user2 295 | // 剩余的messages放到system_prompt：system + user1 + assistant1 296 | expect(result.system_prompt).toBe(`${systemMessage}\nHuman: ${user1}\nAssistant: ${assistant1}`); 297 | 298 | // prompt包含倒序的最后3条消息 299 | const expectedPrompt = `${user2}\nAssistant: ${assistant2}\n${user3}`; 300 | expect(result.prompt).toBe(expectedPrompt); 301 | }); 302 | 303 | // === 空内容和异常情况测试 === 304 | 305 | it('应该正确处理空字符串内容', () => { 306 | const messages: OpenAIMessage[] = [ 307 | { role: 'system', content: '' }, 308 | { role: 'user', content: '' }, 309 | { role: 'assistant', content: '' }, 310 | { role: 'user', content: 'Final message' } 311 | ]; 312 | 313 | const result = convertMessagesToFalPrompt(messages); 314 | 315 | // 空字符串消息被过滤后，只剩下最后一条有效的用户消息 316 | expect(result.error).toBeUndefined(); 317 | expect(result.system_prompt).toBe(''); 318 | expect(result.prompt).toBe('Final message'); 319 | }); 320 | 321 | it('应该正确处理只有assistant消息结尾的情况', () => { 322 | const messages: OpenAIMessage[] = [ 323 | { role: 'user', content: 'Question' }, 324 | { role: 'assistant', content: 'Answer' } 325 | ]; 326 | 327 | const result = convertMessagesToFalPrompt(messages); 328 | 329 | // 倒序遍历至多取3条user/assistant消息放到prompt：Answer + Question 330 | // 最后一条消息不是用户消息，按照要求格式处理 331 | expect(result.system_prompt).toBe(''); 332 | expect(result.prompt).toBe('Question\nAssistant: Answer'); 333 | expect(result.error).toBeUndefined(); 334 | }); 335 | 336 | it('应该过滤掉空内容消息', () => { 337 | const messages: OpenAIMessage[] = [ 338 | { role: 'system', content: 'You are a helpful assistant.' }, 339 | { role: 'user', content: 'First question' }, 340 | { role: 'assistant', content: '' }, // 空字符串，应该被过滤 341 | { role: 'user', content: null }, // null，应该被过滤 342 | { role: 'assistant', content: ' ' }, // 只有空格，应该被过滤 343 | { role: 'user', content: 'Final question' } 344 | ]; 345 | 346 | const result = convertMessagesToFalPrompt(messages); 347 | 348 | // 空的assistant消息被过滤后，只剩下2条用户消息：First question 和 Final question 349 | expect(result.system_prompt).toBe('You are a helpful assistant.'); 350 | // 倒序遍历至多取3条消息，这里只有2条：Final question + First question 351 | expect(result.prompt).toBe('First question\nFinal question'); 352 | expect(result.error).toBeUndefined(); 353 | }); 354 | }); -------------------------------------------------------------------------------- /src/index.ts: -------------------------------------------------------------------------------- 1 | /** 2 | * Welcome to Cloudflare Workers! This is your first worker. 3 | * 4 | * - Run `npm run dev` in your terminal to start a development server 5 | * - Open a browser tab at http://localhost:8787/ to see your worker in action 6 | * - Run `npm run deploy` to publish your worker 7 | * 8 | * Bind resources to your worker in `wrangler.jsonc`. After adding bindings, a type definition for the 9 | * `Env` object can be regenerated with `npm run cf-typegen`. 10 | * 11 | * Learn more at https://developers.cloudflare.com/workers/ 12 | */ 13 | 14 | import { fal } from '@fal-ai/client'; 15 | 16 | // Define the expected environment variables/secrets 17 | interface Env { 18 | /** 19 | * Comma-separated list of Fal AI API keys for redundancy. 20 | * Example: "key1,key2,key3" 21 | * Configure this secret in your Cloudflare dashboard or wrangler.jsonc. 22 | */ 23 | FAL_KEY: string; 24 | /** 25 | * Comma-separated list of allowed API keys for client authentication. 26 | * Example: "clientkey1,clientkey2,verysecretkey" 27 | * Configure this secret in your Cloudflare dashboard or wrangler.jsonc. 28 | */ 29 | API_KEY: string; 30 | /** 31 | * Comma-separated list of allowed origins for CORS. 32 | * Example: "https://example.com,https://app.example.com" 33 | * If not set, defaults to '*' (all origins). 34 | * Configure this secret in your Cloudflare dashboard or wrangler.jsonc. 35 | */ 36 | ALLOWED_ORIGINS?: string; 37 | } 38 | 39 | // OpenAI Request/Response Types (Simplified) 40 | export interface OpenAIMessage { 41 | role: 'system' | 'user' | 'assistant'; 42 | content: string | null; 43 | } 44 | 45 | interface OpenAIChatCompletionRequest { 46 | model: string; 47 | messages: OpenAIMessage[]; 48 | stream?: boolean; 49 | // We can add other OpenAI parameters here if needed, but keep them simple for now 50 | // e.g., max_tokens, temperature, etc. 51 | } 52 | 53 | // Type for supported Fal Model IDs based on the constant array 54 | // This ensures type safety when passing to the fal client 55 | type FalModelId = typeof FAL_SUPPORTED_MODELS[number]; 56 | 57 | interface FalInput { 58 | model: FalModelId; // Use the specific type 59 | prompt: string; 60 | system_prompt?: string; 61 | // Fal-specific parameters can be added here if needed 62 | } 63 | 64 | // Payload for fal.stream or fal.subscribe 65 | interface FalPayload { 66 | input: FalInput; 67 | // Add other stream/subscribe options if needed 68 | } 69 | 70 | // Simple type for log entries in the stream 71 | interface LogEntry { 72 | message: string; 73 | level?: string; // Optional level property 74 | } 75 | 76 | // Type for Fal Stream Event Data 77 | interface FalStreamEventData { 78 | output: string; 79 | partial?: boolean; 80 | error?: any; // Or a more specific type if known 81 | logs?: LogEntry[]; 82 | } 83 | 84 | // Type for Fal Stream Event (passed in iterator) 85 | interface FalStreamEvent { 86 | output: string; 87 | partial?: boolean; 88 | error?: any; // Or a more specific type if known 89 | logs?: LogEntry[]; 90 | // Other potential properties from the stream event can be added here 91 | } 92 | 93 | // Type for Fal Subscribe Result 94 | interface FalSubscribeResult { 95 | data: FalStreamEventData; // Assuming subscribe result structure matches stream event data 96 | requestId: string; 97 | // Other potential top-level properties can be added here 98 | } 99 | 100 | // === Global Definitions === 101 | const SYSTEM_PROMPT_LIMIT = 4800; 102 | 103 | const FAL_SUPPORTED_MODELS = [ 104 | "anthropic/claude-3.7-sonnet", 105 | "anthropic/claude-3.5-sonnet", 106 | "anthropic/claude-3-5-haiku", 107 | "anthropic/claude-3-haiku", 108 | "google/gemini-pro-1.5", 109 | "google/gemini-flash-1.5", 110 | "google/gemini-flash-1.5-8b", 111 | "google/gemini-2.0-flash-001", 112 | "meta-llama/llama-3.2-1b-instruct", 113 | "meta-llama/llama-3.2-3b-instruct", 114 | "meta-llama/llama-3.1-8b-instruct", 115 | "meta-llama/llama-3.1-70b-instruct", 116 | "openai/gpt-4o-mini", 117 | "openai/gpt-4o", 118 | "deepseek/deepseek-r1", 119 | "meta-llama/llama-4-maverick", 120 | "meta-llama/llama-4-scout" 121 | ] as const; // Use 'as const' for stricter type checking 122 | 123 | // Helper function to get owner from model ID 124 | const getOwner = (modelId: string): string => { 125 | if (modelId && modelId.includes('/')) { 126 | return modelId.split('/')[0]; 127 | } 128 | return 'https://fal.ai'; // Default owner 129 | }; 130 | 131 | /** 132 | * 将 OpenAI 格式的消息转换为 Fal AI 格式的 prompt 和 system_prompt 133 | * 134 | * 核心逻辑：倒序遍历 messages，至多取 3 条 user/assistant 消息放到 prompt 部分， 135 | * chat_history 最多包含 2 条消息（user + assistant），最后一个用户消息是最新提问，不属于对话历史 136 | * 137 | * @param messages - OpenAI 格式的消息数组 138 | * @returns 包含 system_prompt、prompt 和可选错误信息的对象 139 | * 140 | * @example 141 | * // 基本用法：系统消息 + 用户消息 142 | * const messages = [ 143 | * { role: 'system', content: 'You are a helpful assistant.' }, 144 | * { role: 'user', content: 'Hello, how are you?' } 145 | * ]; 146 | * const result = convertMessagesToFalPrompt(messages); 147 | * // result.system_prompt: 'You are a helpful assistant.' 148 | * // result.prompt: 'Hello, how are you?' 149 | * 150 | * @example 151 | * // 多轮对话：最后一条是用户消息 152 | * const messages = [ 153 | * { role: 'system', content: 'You are helpful.' }, 154 | * { role: 'user', content: 'What is AI?' }, 155 | * { role: 'assistant', content: 'AI is artificial intelligence.' }, 156 | * { role: 'user', content: 'Tell me more.' } 157 | * ]; 158 | * const result = convertMessagesToFalPrompt(messages); 159 | * // result.system_prompt: 'You are helpful.\n' 160 | * // result.prompt: 'What is AI?\nAssistant: AI is artificial intelligence.\n\nTell me more.' 161 | * 162 | * @example 163 | * // 多轮对话：最后一条不是用户消息 164 | * const messages = [ 165 | * { role: 'user', content: 'Hello' }, 166 | * { role: 'assistant', content: 'Hi there!' } 167 | * ]; 168 | * const result = convertMessagesToFalPrompt(messages); 169 | * // result.system_prompt: '\nHuman: Hello\nAssistant: Hi there!\n' 170 | * // result.prompt: '' 171 | * 172 | * @description 173 | * 实现逻辑： 174 | * 1. **系统消息处理**：只使用最后一个非空系统消息，如果超出 SYSTEM_PROMPT_LIMIT 则返回错误 175 | * 2. **消息过滤**：自动过滤空内容消息（null、undefined、空字符串或纯空格） 176 | * 3. **倒序遍历**：取最后 3 条消息，根据最后一条消息类型分两种情况： 177 | * 178 | * **情况 A - 最后一条是用户消息**： 179 | * - 取倒数第 3、第 2 条作为 chat_history（最多 2 条：user + assistant） 180 | * - system_prompt: `系统消息\n` 181 | * - prompt: `\nAssistant: \n\n<最新用户消息>` 182 | * 183 | * **情况 B - 最后一条不是用户消息**： 184 | * - 取最后 2 条消息作为 chat_history，放在 system_prompt 中 185 | * - system_prompt: `系统消息\n\nHuman: \nAssistant: \n` 186 | * - prompt: `""`（空字符串） 187 | * 188 | * 4. **格式约定**： 189 | * - prompt 中会自动拼接 Human 消息，所以 user 消息不需要 "Human:" 前缀 190 | * - system_prompt 中的 user 消息需要 "Human:" 前缀 191 | * - assistant 消息始终使用 "Assistant:" 前缀 192 | * 193 | * @note 194 | * - 字符限制：系统消息长度不能超过 SYSTEM_PROMPT_LIMIT (4800) 字符 195 | * - 消息数量：最多处理最近的 3 条对话消息（倒数第 1、2、3 条） 196 | * - 历史限制：chat_history 最多包含 2 条消息，避免 prompt 过长 197 | * - 错误处理：系统消息超限时返回错误，其他情况尽力处理 198 | */ 199 | export function convertMessagesToFalPrompt(messages: OpenAIMessage[]): { system_prompt: string; prompt: string; error?: string } { 200 | // 第一步：过滤空内容消息，分离系统消息和对话消息 201 | const filtered_messages: OpenAIMessage[] = []; 202 | let system_message_content = ""; 203 | 204 | for (const message of messages) { 205 | const content = (message.content === null || message.content === undefined) ? "" : String(message.content).trim(); 206 | if (content.length > 0) { 207 | if (message.role === 'system') { 208 | system_message_content = content; // 只保留最后一个非空系统消息 209 | } else { 210 | filtered_messages.push({ 211 | ...message, 212 | content: content 213 | }); 214 | } 215 | } 216 | } 217 | 218 | // 检查系统消息长度限制 219 | if (system_message_content.length > SYSTEM_PROMPT_LIMIT) { 220 | return { 221 | system_prompt: "", 222 | prompt: "", 223 | error: `System message too long: ${system_message_content.length} characters exceeds limit of ${SYSTEM_PROMPT_LIMIT} characters` 224 | }; 225 | } 226 | 227 | // 如果没有对话消息，直接返回 228 | if (filtered_messages.length === 0) { 229 | return { 230 | system_prompt: system_message_content, 231 | prompt: "" 232 | }; 233 | } 234 | 235 | // 第二步：倒序遍历messages，至多取3条user/assistant消息放到prompt部分 236 | const prompt_messages = filtered_messages.slice(-3); // 取最后3条消息 237 | const remaining_messages = filtered_messages.slice(0, -3); // 剩余的消息 238 | 239 | // 第三步：构建prompt部分 240 | let prompt_parts: string[] = []; 241 | 242 | for (const message of prompt_messages) { 243 | if (message.role === 'user') { 244 | prompt_parts.push(String(message.content)); 245 | } else if (message.role === 'assistant') { 246 | prompt_parts.push(`Assistant: ${String(message.content)}`); 247 | } 248 | } 249 | 250 | const final_prompt = prompt_parts.join('\n'); 251 | 252 | // 第四步：构建system_prompt部分 253 | let system_prompt_parts: string[] = []; 254 | 255 | // 添加系统消息（如果存在） 256 | if (system_message_content.length > 0) { 257 | system_prompt_parts.push(system_message_content); 258 | } 259 | 260 | // 添加剩余的对话消息 261 | for (const message of remaining_messages) { 262 | if (message.role === 'user') { 263 | system_prompt_parts.push(`Human: ${String(message.content)}`); 264 | } else if (message.role === 'assistant') { 265 | system_prompt_parts.push(`Assistant: ${String(message.content)}`); 266 | } 267 | } 268 | 269 | let final_system_prompt = system_prompt_parts.join('\n'); 270 | 271 | // 第五步：检查system_prompt字符限制并截断 272 | if (final_system_prompt.length > SYSTEM_PROMPT_LIMIT) { 273 | // 优先保留系统消息，然后从最新的对话开始截断 274 | const system_part = system_message_content; 275 | let remaining_space = SYSTEM_PROMPT_LIMIT - system_part.length - 1; // -1 for newline 276 | 277 | if (remaining_space <= 0) { 278 | final_system_prompt = system_part; 279 | } else { 280 | const conversation_parts: string[] = []; 281 | 282 | // 倒序添加剩余对话，确保不超过字符限制 283 | for (let i = remaining_messages.length - 1; i >= 0; i--) { 284 | const message = remaining_messages[i]; 285 | let message_text = ""; 286 | 287 | if (message.role === 'user') { 288 | message_text = `Human: ${String(message.content)}`; 289 | } else if (message.role === 'assistant') { 290 | message_text = `Assistant: ${String(message.content)}`; 291 | } 292 | 293 | if (message_text.length + 1 <= remaining_space) { // +1 for newline 294 | conversation_parts.unshift(message_text); 295 | remaining_space -= (message_text.length + 1); 296 | } else { 297 | break; // 无法添加更多消息 298 | } 299 | } 300 | 301 | if (system_part.length > 0 && conversation_parts.length > 0) { 302 | final_system_prompt = system_part + '\n' + conversation_parts.join('\n'); 303 | } else if (system_part.length > 0) { 304 | final_system_prompt = system_part; 305 | } else { 306 | final_system_prompt = conversation_parts.join('\n'); 307 | } 308 | } 309 | } 310 | 311 | return { 312 | system_prompt: final_system_prompt, 313 | prompt: final_prompt 314 | }; 315 | } 316 | 317 | // Type guard to check if a string is a valid FalModelId 318 | function isValidFalModelId(modelId: string): modelId is FalModelId { 319 | return (FAL_SUPPORTED_MODELS as readonly string[]).includes(modelId); 320 | } 321 | 322 | // === CORS Utilities === 323 | // Default CORS headers 324 | function getCorsHeaders(): HeadersInit { 325 | return { 326 | 'Access-Control-Allow-Origin': '*', 327 | 'Access-Control-Allow-Methods': 'GET, POST, OPTIONS, PUT, DELETE, PATCH', 328 | 'Access-Control-Allow-Headers': '*', 329 | 'Access-Control-Max-Age': '86400', // Cache preflight for 1 day 330 | }; 331 | } 332 | 333 | // Create a response with CORS headers included 334 | function createCorsResponse(body: string | null, options: ResponseInit): Response { 335 | const corsHeaders = getCorsHeaders(); 336 | const headers = { ...options.headers, ...corsHeaders }; 337 | 338 | return new Response(body, { 339 | ...options, 340 | headers 341 | }); 342 | } 343 | 344 | /** 345 | * Attempts a Fal API request (stream or subscribe) with key rotation on failure. 346 | * @param falKeys Array of Fal API keys. 347 | * @param falPayload Payload for the fal request. 348 | * @param stream Whether to use streaming. 349 | * @param modelToReport The original model name requested by the user for reporting. 350 | * @param ctx ExecutionContext for streaming requests. 351 | * @returns For stream=true: { readable: ReadableStream }. For stream=false: FalSubscribeResult. 352 | * @throws Throws an error if all keys fail or if a non-retryable error occurs. 353 | */ 354 | async function tryFalRequest( 355 | falKeys: string[], 356 | falPayload: FalPayload, 357 | stream: boolean, 358 | modelToReport: string, // Pass the original model name 359 | ctx?: ExecutionContext 360 | ): Promise<{ readable: ReadableStream } | FalSubscribeResult> { 361 | let lastError: any = null; 362 | 363 | if (falKeys.length === 0) { 364 | throw new Error("No Fal API keys configured."); 365 | } 366 | 367 | for (let i = 0; i < falKeys.length; i++) { 368 | const key = falKeys[i]; 369 | console.log(`Attempting Fal request with key index ${i}`); 370 | try { 371 | // Configure the client with the current key for this attempt 372 | fal.config({ credentials: key }); 373 | 374 | if (stream && ctx) { 375 | // --- Stream Handling --- 376 | console.log(`Initiating fal.stream with key index ${i}... Payload:`, JSON.stringify(falPayload, null, 2)); 377 | const falStream = await fal.stream("fal-ai/any-llm", falPayload as any); // Use the prepared payload object 378 | console.log(`fal.stream initiated successfully with key index ${i}.`); 379 | 380 | // Set up the response stream 381 | const { readable, writable } = new TransformStream(); 382 | const writer = writable.getWriter(); 383 | const encoder = new TextEncoder(); 384 | 385 | // Start background processing for the stream events 386 | const streamProcessing = (async () => { 387 | try { 388 | let previousOutput = ''; 389 | for await (const event of falStream) { 390 | // console.log(`Fal Stream Event (Key ${i}):`, event); // Verbose logging 391 | const currentOutput = (event && typeof event.output === 'string') ? event.output : ''; 392 | const isPartial = (event && typeof event.partial === 'boolean') ? event.partial : true; // Assume partial if not specified 393 | const errorInfo = (event && event.error) ? event.error : null; 394 | const logs = (event && Array.isArray((event as any).logs)) ? (event as any).logs : []; 395 | 396 | logs.forEach((log: LogEntry) => console.log(`[Fal Log - Key ${i}] ${log.message}`)); 397 | 398 | if (errorInfo) { 399 | console.error(`Error received in fal stream event (Key ${i}):`, errorInfo); 400 | const errorChunk = { id: `chatcmpl-${Date.now()}-error`, object: "chat.completion.chunk", created: Math.floor(Date.now() / 1000), model: modelToReport, choices: [{ index: 0, delta: {}, finish_reason: "error", logprobs: null, message: { role: 'assistant', content: `Fal Stream Error: ${JSON.stringify(errorInfo)}` } }] }; 401 | await writer.write(encoder.encode(`data: ${JSON.stringify(errorChunk)}\n\n`)); 402 | throw new Error(`Fal Stream Error: ${JSON.stringify(errorInfo)}`); // Throw to stop processing this stream 403 | } 404 | 405 | let deltaContent = ''; 406 | if (currentOutput.startsWith(previousOutput)) { 407 | deltaContent = currentOutput.substring(previousOutput.length); 408 | } else if (currentOutput.length > 0) { 409 | console.warn(`Fal stream output mismatch (Key ${i}). Sending full current output as delta.`); 410 | deltaContent = currentOutput; 411 | previousOutput = ''; // Reset 412 | } 413 | if (currentOutput.length > 0) { 414 | previousOutput = currentOutput; 415 | } 416 | 417 | if (deltaContent || !isPartial) { 418 | const choice = { 419 | index: 0, 420 | delta: { content: deltaContent }, 421 | finish_reason: isPartial === false ? "stop" : null, 422 | logprobs: null 423 | }; 424 | const openAIChunk = { 425 | id: `chatcmpl-${Date.now()}-${Math.random().toString(36).substring(2, 15)}`, 426 | object: "chat.completion.chunk", 427 | created: Math.floor(Date.now() / 1000), 428 | model: modelToReport, // Report the originally requested model 429 | choices: [choice] 430 | }; 431 | await writer.write(encoder.encode(`data: ${JSON.stringify(openAIChunk)}\n\n`)); 432 | } 433 | } 434 | console.log(`Fal stream iteration finished successfully (Key ${i}).`); 435 | await writer.write(encoder.encode(`data: [DONE]\n\n`)); 436 | 437 | } catch (streamError: any) { 438 | console.error(`Error during fal stream processing (Key ${i}):`, streamError); 439 | try { 440 | // Attempt to write an error chunk to the client stream 441 | const errorDetails = (streamError instanceof Error) ? streamError.message : JSON.stringify(streamError); 442 | const errorChunk = { error: { message: "Stream processing error", type: "proxy_error", param: null, code: null, details: errorDetails } }; 443 | await writer.write(encoder.encode(`data: ${JSON.stringify(errorChunk)}\n\n`)); 444 | // Always attempt to send DONE, even after error chunk 445 | await writer.write(encoder.encode(`data: [DONE]\n\n`)); 446 | } catch (writeError) { 447 | console.error(`Error writing final stream error message (Key ${i}):`, writeError); 448 | } 449 | // Do not re-throw here, as the initial response has already been sent. 450 | // The error is reported within the stream itself. 451 | } finally { 452 | try { 453 | console.log(`Closing stream writer (Key ${i})...`); 454 | await writer.close(); 455 | console.log(`Stream writer closed (Key ${i}).`); 456 | } catch (closeError) { 457 | console.error(`Error closing stream writer (Key ${i}):`, closeError); 458 | } 459 | } 460 | })(); 461 | 462 | ctx.waitUntil(streamProcessing); // Allow background processing 463 | 464 | // Return the readable stream immediately for the client 465 | return { readable }; // Indicate success for this key attempt 466 | 467 | } else { 468 | // --- Non-Stream Handling --- 469 | console.log(`Executing fal.subscribe with key index ${i}... Payload:`, JSON.stringify(falPayload, null, 2)); 470 | const result = await fal.subscribe("fal-ai/any-llm", falPayload as any) as FalSubscribeResult; 471 | console.log(`fal.subscribe successful with key index ${i}.`); 472 | 473 | // Log any embedded logs or errors from the non-stream result 474 | if (result?.data?.logs && Array.isArray(result.data.logs)) { 475 | result.data.logs.forEach((log: LogEntry) => console.log(`[Fal Log - Key ${i}] ${log.message}`)); 476 | } 477 | if (result?.data?.error) { 478 | // Treat error within data as failure for this key 479 | console.error(`Fal-ai returned an error in non-stream mode (Key ${i}):`, result.data.error); 480 | throw new Error(`Fal error in response data: ${JSON.stringify(result.data.error)}`); 481 | } 482 | 483 | return result; // Return successful result 484 | } 485 | 486 | } catch (error: any) { 487 | lastError = error; // Store the error from this attempt 488 | console.warn(`Fal request attempt failed for key index ${i}:`, error.message || error); 489 | 490 | // Check if the error suggests a key-specific issue (e.g., auth, rate limit, server error) 491 | // Fal client might throw custom errors or HTTP status codes might be attached. 492 | // This check might need refinement based on actual errors thrown by @fal-ai/client. 493 | // Assuming status property exists for HTTP errors. 494 | const status = error?.status || error?.response?.status; 495 | const isRetryableError = status === 401 || status === 403 || status === 429 || (status >= 500 && status < 600) || error.message?.includes('authentication') || error.message?.includes('rate limit'); 496 | 497 | 498 | if (isRetryableError && (i < falKeys.length - 1)) { 499 | console.log(`Error is potentially key-related or transient (status: ${status}). Trying next key.`); 500 | continue; // Try the next key 501 | } else { 502 | console.error(`Non-retryable error (status: ${status}) or final key attempt failed for key index ${i}.`); 503 | throw error; // Re-throw the error if it's not retryable or it's the last key 504 | } 505 | } 506 | } 507 | 508 | // If the loop finishes without returning/throwing successfully, it means all keys failed. 509 | console.error("All Fal API key attempts failed."); 510 | // Throw the last recorded error 511 | throw new Error(`All Fal API key attempts failed. Last error: ${lastError?.message || lastError}`); 512 | } 513 | 514 | // === Main Fetch Handler === 515 | export default { 516 | async fetch(request: Request, env: Env, ctx: ExecutionContext): Promise { 517 | const url = new URL(request.url); 518 | const path = url.pathname; 519 | const method = request.method; 520 | 521 | // Handle CORS preflight requests (OPTIONS) 522 | if (method === 'OPTIONS') { 523 | return new Response(null, { 524 | status: 204, 525 | headers: getCorsHeaders() 526 | }); 527 | } 528 | 529 | let falKeys: string[] = []; 530 | 531 | // --- Authentication & Fal Key Parsing --- (Run for all relevant endpoints) 532 | if (path.startsWith('/v1/')) { // Only process /v1 routes 533 | // --- Worker Authentication --- 534 | const authHeader = request.headers.get('Authorization'); 535 | const apiKeysString = env.API_KEY; 536 | 537 | if (!apiKeysString) { 538 | console.error("API_KEY secret is not set."); 539 | return createCorsResponse( 540 | JSON.stringify({ error: { message: 'Server configuration error: API Key secret missing.', type: 'server_error' } }), 541 | { status: 500, headers: { 'Content-Type': 'application/json' } } 542 | ); 543 | } 544 | 545 | const allowedApiKeys = apiKeysString.split(',').map(k => k.trim()).filter(k => k.length > 0); 546 | if (allowedApiKeys.length === 0) { 547 | console.error("API_KEY secret is set but contains no valid keys after parsing."); 548 | return createCorsResponse( 549 | JSON.stringify({ error: { message: 'Server configuration error: No valid API Keys found in secret.', type: 'server_error' } }), 550 | { status: 500, headers: { 'Content-Type': 'application/json' } } 551 | ); 552 | } 553 | 554 | const providedApiKey = authHeader?.startsWith('Bearer ') ? authHeader.substring(7) : null; 555 | 556 | if (!providedApiKey || !allowedApiKeys.includes(providedApiKey)) { 557 | console.error(`Worker auth failed. Header: ${authHeader ? 'Present' : 'Missing'}, Key check: Provided key "${providedApiKey}" not in allowed list.`); 558 | return createCorsResponse( 559 | JSON.stringify({ error: { message: 'Incorrect API key provided.', type: 'invalid_request_error', param: null, code: 'invalid_api_key'} }), 560 | { status: 401, headers: { 'Content-Type': 'application/json' } } 561 | ); 562 | } 563 | 564 | // --- Parse Fal Keys --- 565 | if (!env.FAL_KEY) { 566 | console.error("FAL_KEY secret is not set."); 567 | return createCorsResponse( 568 | JSON.stringify({ error: { message: 'Server configuration error: Fal Key missing.', type: 'server_error'} }), 569 | { status: 500, headers: { 'Content-Type': 'application/json' } } 570 | ); 571 | } 572 | falKeys = env.FAL_KEY.split(',').map(k => k.trim()).filter(k => k.length > 0); 573 | if (falKeys.length === 0) { 574 | console.error("FAL_KEY secret is set but contains no valid keys after parsing."); 575 | return createCorsResponse( 576 | JSON.stringify({ error: { message: 'Server configuration error: No valid Fal Keys found.', type: 'server_error'} }), 577 | { status: 500, headers: { 'Content-Type': 'application/json' } } 578 | ); 579 | } 580 | console.log(`Found ${falKeys.length} Fal API key(s).`); 581 | // Note: fal.config() is now called inside tryFalRequest for each attempt 582 | 583 | } else if (path === '/') { 584 | // Allow root path without auth 585 | } else { 586 | // Any other path is Not Found 587 | return createCorsResponse( 588 | JSON.stringify({ error: { message: `Invalid path: ${path}`, type: 'invalid_request_error'} }), 589 | { status: 404, headers: { 'Content-Type': 'application/json' } } 590 | ); 591 | } 592 | 593 | // --- Routing --- 594 | 595 | // GET /v1/models 596 | if (method === 'GET' && path === '/v1/models') { 597 | try { 598 | const modelsData = FAL_SUPPORTED_MODELS.map(modelId => ({ 599 | id: modelId, 600 | object: "model", 601 | created: Math.floor(Date.now() / 1000), 602 | owned_by: getOwner(modelId) 603 | })); 604 | return createCorsResponse( 605 | JSON.stringify({ object: "list", data: modelsData }), 606 | { headers: { 'Content-Type': 'application/json' } } 607 | ); 608 | } catch (error: any) { 609 | console.error("Error processing GET /v1/models:", error); 610 | return createCorsResponse( 611 | JSON.stringify({ error: { message: `Failed to retrieve model list: ${error.message}`, type: 'server_error'} }), 612 | { status: 500, headers: { 'Content-Type': 'application/json' } } 613 | ); 614 | } 615 | } 616 | 617 | // POST /v1/chat/completions 618 | if (method === 'POST' && path === '/v1/chat/completions') { 619 | let requestBody: OpenAIChatCompletionRequest; 620 | try { 621 | requestBody = await request.json(); 622 | } catch (error) { 623 | return createCorsResponse( 624 | JSON.stringify({ error: { message: 'Invalid JSON request body', type: 'invalid_request_error'} }), 625 | { status: 400, headers: { 'Content-Type': 'application/json' } } 626 | ); 627 | } 628 | 629 | const { model: requestedModel, messages, stream = false } = requestBody; 630 | 631 | if (!requestedModel || !messages || !Array.isArray(messages) || messages.length === 0) { 632 | return createCorsResponse( 633 | JSON.stringify({ error: { message: 'Missing or invalid parameters: model and messages array are required.', type: 'invalid_request_error'} }), 634 | { status: 400, headers: { 'Content-Type': 'application/json' } } 635 | ); 636 | } 637 | 638 | // Validate and determine the model to use 639 | let modelToUse: FalModelId; 640 | if (isValidFalModelId(requestedModel)) { 641 | modelToUse = requestedModel; 642 | } else { 643 | console.error(`Error: Requested model '${requestedModel}' is not supported.`); 644 | return createCorsResponse( 645 | JSON.stringify({ 646 | error: { 647 | message: `The model \`${requestedModel}\` does not exist or is not supported by this endpoint.`, 648 | type: 'invalid_request_error', 649 | param: 'model', 650 | code: 'model_not_found' 651 | } 652 | }), 653 | { status: 400, headers: { 'Content-Type': 'application/json' } } 654 | ); 655 | } 656 | 657 | 658 | 659 | try { 660 | const convertResult = convertMessagesToFalPrompt(messages); 661 | 662 | // Check for conversion error 663 | if (convertResult.error) { 664 | return createCorsResponse( 665 | JSON.stringify({ error: { message: convertResult.error, type: 'invalid_request_error' } }), 666 | { status: 400, headers: { 'Content-Type': 'application/json' } } 667 | ); 668 | } 669 | 670 | const { prompt, system_prompt } = convertResult; 671 | const falInput: FalInput = { 672 | model: modelToUse, 673 | prompt: prompt, 674 | }; 675 | if (system_prompt) { 676 | falInput.system_prompt = system_prompt; 677 | } 678 | 679 | // Prepare the final payload for Fal 680 | const falPayload: FalPayload = { input: falInput }; 681 | 682 | console.log(`Preparing request for fal-ai/any-llm. Model: ${falInput.model}, Stream: ${stream}`); 683 | 684 | // Use the tryFalRequest function with key rotation 685 | const result = await tryFalRequest(falKeys, falPayload, stream, requestedModel, ctx); 686 | 687 | if (stream) { 688 | // --- Stream Handling --- 689 | // tryFalRequest handles the stream setup internally if successful 690 | // We just need to return the response with the readable stream 691 | if ('readable' in result) { 692 | // For streaming responses, we need to add CORS headers but keep the stream 693 | const corsHeaders = getCorsHeaders(); 694 | return new Response(result.readable, { 695 | headers: { 696 | 'Content-Type': 'text/event-stream; charset=utf-8', 697 | 'Cache-Control': 'no-cache', 698 | 'Connection': 'keep-alive', 699 | ...corsHeaders 700 | } 701 | }); 702 | } else { 703 | // This case should theoretically not happen if stream=true and no error was thrown 704 | console.error("tryFalRequest returned unexpected result for stream=true"); 705 | throw new Error("Internal server error during stream setup."); 706 | } 707 | 708 | } else { 709 | // --- Non-Stream Handling --- 710 | // tryFalRequest returns the FalSubscribeResult directly if successful 711 | const falResult = result as FalSubscribeResult; // Type assertion 712 | 713 | // Log the raw result for debugging (optional) 714 | // console.log("Raw non-stream result from fal-ai:", JSON.stringify(falResult, null, 2)); 715 | 716 | // Access properties correctly from the data object returned by tryFalRequest 717 | const outputContent = falResult?.data?.output ?? ""; 718 | const reqId = falResult?.requestId || Date.now().toString(); 719 | 720 | const openAIResponse = { 721 | id: `chatcmpl-${reqId}`, 722 | object: "chat.completion", 723 | created: Math.floor(Date.now() / 1000), 724 | model: requestedModel, // Report the originally requested model 725 | choices: [{ 726 | index: 0, 727 | message: { 728 | role: "assistant", 729 | content: outputContent 730 | }, 731 | finish_reason: "stop", 732 | logprobs: null 733 | }], 734 | usage: { prompt_tokens: null, completion_tokens: null, total_tokens: null }, 735 | system_fingerprint: null, 736 | }; 737 | return createCorsResponse( 738 | JSON.stringify(openAIResponse), 739 | { headers: { 'Content-Type': 'application/json' } } 740 | ); 741 | } 742 | } catch (error: any) { 743 | // Catch errors from tryFalRequest (e.g., all keys failed) or other processing errors 744 | console.error("Error during Fal request processing or after retries:", error); 745 | const errorMessage = (error instanceof Error) ? error.message : JSON.stringify(error); 746 | return createCorsResponse( 747 | JSON.stringify({ error: { message: `Error processing request: ${errorMessage}`, type: 'server_error' } }), 748 | { status: 500, headers: { 'Content-Type': 'application/json' } } 749 | ); 750 | } 751 | } 752 | 753 | // Default response for root path or other unhandled routes 754 | return createCorsResponse( 755 | JSON.stringify({ status: 'ok', message: 'Fal AI-powered OpenAI Proxy API' }), 756 | { headers: { 'Content-Type': 'application/json' } } 757 | ); 758 | } 759 | }; -------------------------------------------------------------------------------- /pnpm-lock.yaml: -------------------------------------------------------------------------------- 1 | lockfileVersion: '9.0' 2 | 3 | settings: 4 | autoInstallPeers: true 5 | excludeLinksFromLockfile: false 6 | 7 | importers: 8 | 9 | .: 10 | dependencies: 11 | '@fal-ai/client': 12 | specifier: ^1.5.0 13 | version: 1.5.0 14 | devDependencies: 15 | 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